mkpipe-extractor-clickhouse 0.6.0

Clickhouse extractor for mkpipe.

mkpipe-extractor-clickhouse

ClickHouse extractor plugin for MkPipe. Reads ClickHouse tables using the native clickhouse-spark connector, which uses ClickHouse's binary HTTP protocol for columnar data transfer — faster than JDBC, especially for analytical queries.

Documentation

For more detailed documentation, please visit the GitHub repository.

License

This project is licensed under the Apache 2.0 License - see the LICENSE file for details.

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Connection Configuration

connections:
  clickhouse_source:
    variant: clickhouse
    host: localhost
    port: 8123
    database: source_db
    user: default
    password: mypassword

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Table Configuration

pipelines:
  - name: clickhouse_to_pg
    source: clickhouse_source
    destination: pg_target
    tables:
      - name: events
        target_name: stg_events
        replication_method: full
        fetchsize: 100000

Incremental Replication

      - name: events
        target_name: stg_events
        replication_method: incremental
        iterate_column: updated_at
        iterate_column_type: datetime
        partitions_column: id
        partitions_count: 8
        fetchsize: 50000

Custom SQL

      - name: events
        target_name: stg_events
        replication_method: full
        custom_query: "SELECT id, user_id, event_type, created_at FROM events WHERE {query_filter}"

Use {query_filter} as a placeholder — it is replaced with the incremental WHERE clause on incremental runs, or WHERE 1=1 on full runs.

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Read Parallelism

ClickHouse extractor uses JDBC with Spark's native partition support. For large tables, set partitions_column and partitions_count to read in parallel:

      - name: events
        target_name: stg_events
        replication_method: incremental
        iterate_column: created_at
        iterate_column_type: datetime
        partitions_column: id      # numeric column to split on
        partitions_count: 8        # number of parallel JDBC partitions
        fetchsize: 50000

How it works

• Spark reads the min/max of partitions_column and divides the range into partitions_count equal slices
• Each slice is fetched by a separate Spark task via a separate JDBC connection
• fetchsize controls how many rows each connection fetches per round-trip

Performance Notes

• Full replication: partitioning is not applied (only works with incremental).
• partitions_column should be a numeric column with good distribution (e.g. primary key).
• fetchsize: ClickHouse is a columnar store — large fetchsize (50,000–200,000) works well.
• ClickHouse handles large scans efficiently; for distributed tables parallelism is less critical than for row-based databases.

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All Table Parameters

Parameter	Type	Default	Description
name	string	required	ClickHouse table name
target_name	string	required	Destination table name
replication_method	full / incremental	full	Replication strategy
iterate_column	string	—	Column used for incremental watermark
iterate_column_type	int / datetime	—	Type of iterate_column
partitions_column	string	same as iterate_column	Column to split JDBC reads on
partitions_count	int	10	Number of parallel JDBC partitions
fetchsize	int	100000	Rows per JDBC fetch
custom_query	string	—	Override SQL with {query_filter} placeholder
custom_query_file	string	—	Path to SQL file (relative to sql/ dir)
write_partitions	int	—	Coalesce to N partitions before writing
tags	list	[]	Tags for selective pipeline execution
pass_on_error	bool	false	Skip table on error instead of failing